Translation of abstract (English)

This work is about images from a high throughput microscopy. Because of the huge amount of images, the analysis has to be processed in an automatic way. There are two approaches: the offline processing, image processing on a computer cluster, and the online processing, image processing of the streaming data from the sensors. To cope with the image data in the offline processing this work uses graphics cards as accelerators and shows an CUDA implementation of the Haralick Texture Features. The accelerated version achieves a speed up of around 1000 against a CPU solution. This offers the biologist the opportunity to do more tests and leads to a faster gain of knowledge. The online processing uses FPGAs which are easy to connect to the sensors. The biologists have the constraint to adapt the algorithm for their future needs. This work presents a developed OpenCL to FPGA compiler prototype. The algorithm can be written in OpenCL and compiled for the FPGA without any knowledge of any hardware description language. Furthermore, OpenCL is a portable language between several computing architectures. If an algorithm written in OpenCL is too complex for the FPGA compiler due to the existing restrictions, then a compilation for the GPUs in the offline processing environment is still possible.